意思決定依存コスト不確実性下でのロバスト戦略的分類
Robust Strategic Classification under Decision-Dependent Cost Uncertainty
戦略的分類における操作コストが過去のアルゴリズム決定に依存する問題を、二段階ロバスト最適化と意思決定依存不確実性集合でモデル化し、ゲーミング抑制効果を解析した。
著者: Sura Alhanouti, Güzin Bayraksan, Parinaz Naghizadeh
分類: cs.LG, cs.GT
原文アブストラクト
Humans facing algorithmic decision systems have been found to ``game'' them by altering their input data (at a cost to them) in order to favorably change the algorithmic outcomes they receive (at a cost to the algorithm). The growing literature on strategic classification seeks to develop robust machine learning algorithms that account for, and reduce, unwanted strategic behavior. A limitation of these existing works is that they assume the cost of strategic behavior to be fixed and independent of the classifier's decision. In practice, however, manipulation costs evolve and depend on past algorithmic decisions: today's decisions influence tomorrow's costs. This paper proposes and analyzes a two-stage robust optimization framework with a decision-dependent uncertainty set to capture such dependencies. We highlight that awareness of policy-dependent costs not only reduces uncertainty, but also better curtails gaming of the algorithmic system over time.